Education and Learning Management Systems - Learning Analytics Dashboard

 

Problem: Analyzing students' learning performance manually is time-consuming and lacks visual representation.

Solution: Provides a visual dashboard that displays students' grades, progress, and completion rates.

Benefits: Helps instructors make informed decisions based on data, and gives students clear insights into their progress.

Adoption: This can be expanded by integrating more advanced analytics such as predictive performance or recommendations.

Code:

import matplotlib.pyplot as plt

# Sample student performance data

students = {

    'John Doe': {'Python Programming': 85, 'Data Science': 90},

    'Jane Smith': {'Python Programming': 75, 'Data Science': 80},

    'Alice Brown': {'Machine Learning': 65},

}

# Function to visualize performance

def visualize_performance():

    student_names = list(students.keys())

    course_names = list(students[student_names[0]].keys())

    scores = [list(student.values()) for student in students.values()]

    

    fig, ax = plt.subplots()

    ax.boxplot(scores, labels=course_names)

    ax.set_title('Learning Analytics Dashboard')

    ax.set_xlabel('Courses')

    ax.set_ylabel('Scores')

    plt.xticks(rotation=45)

    plt.show()

# Run the dashboard

visualize_performance()